Toward an Error Theory for PIP (Probabilistic Information Processing): Inference Based on an Alternative Formulation of the Data Space

Abstract

Probabilistic Information Processing (PIP) systems, as currently conceived, use experts' intuitive judgments about the diagnostic impact of individual data as inputs for mechanical aggregation by Bayes's theorem. Past research has shown that the posterior odds output by PIP are much more extreme than those arrived at via human aggregation. Because of this superior efficiency PIP-type processing of fallible data has been recommended as an important tool for decision making. The present paper questions the uncritical use in PIP of estimated likelihood ratios as if they were veridical. A theory is developed which incorporates into the inferential process the inherent variability of human judgment. The resulting effect is a decrease in the posterior odds given by PIP.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1973
Accession Number
AD0770568

Entities

People

  • Dennis G. Fryback
  • Ward Edwards

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Engineering
  • Equations
  • Estimators
  • Human Factors Engineering
  • Information Processing
  • Information Systems
  • Judgment
  • Military Research
  • Numbers
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Psychology
  • Random Variables
  • Systems Science

Readers

  • Cellular and Molecular Pathways of Apoptosis.
  • Statistical inference.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space
  • Space - Space Objects